Material Measurement Laboratory,National Institute of Standards and Technology,Gaithersburg MD, 20899,USA.
Materials Science and Engineering,Carnegie Mellon University,Pittsburgh PA, 15213,USA.
Microsc Microanal. 2019 Feb;25(1):21-29. doi: 10.1017/S1431927618015635.
We apply a deep convolutional neural network segmentation model to enable novel automated microstructure segmentation applications for complex microstructures typically evaluated manually and subjectively. We explore two microstructure segmentation tasks in an openly available ultrahigh carbon steel microstructure dataset: segmenting cementite particles in the spheroidized matrix, and segmenting larger fields of view featuring grain boundary carbide, spheroidized particle matrix, particle-free grain boundary denuded zone, and Widmanstätten cementite. We also demonstrate how to combine these data-driven microstructure segmentation models to obtain empirical cementite particle size and denuded zone width distributions from more complex micrographs containing multiple microconstituents. The full annotated dataset is available on materialsdata.nist.gov.
我们应用深度卷积神经网络分割模型，实现了复杂微观结构的新型自动化微观结构分割应用，这些结构通常是手动和主观评估的。我们在一个公开的超高碳钢微观结构数据集上探索了两个微观结构分割任务：分割球化基体中的渗碳体颗粒，以及分割具有晶界碳化物、球化颗粒基体、无颗粒晶界脱碳区和魏氏渗碳体的较大视场。我们还展示了如何结合这些数据驱动的微观结构分割模型，从包含多个微观成分的更复杂的显微照片中获得经验渗碳体颗粒尺寸和脱碳区宽度分布。完整的标注数据集可在 materialsdata.nist.gov 上获得。
